Conditional base maintenance has been gaining widespread acceptance, with numerous sensors attached to equipment for constant monitoring of its operational state, the resulting sensor data being compared with those about the equipment in the normal state for a diagnosis to determine whether the equipment is currently operating normally, the result of the diagnosis being used to conduct maintenance. Conditional base maintenance can rapidly detect aging deterioration of the equipment, so that abnormal states that were not detected before in time base maintenance can now be detected. However, although conventional diagnosis technology can distinguish between the normal state and abnormal state, it has been difficult with such technology to identify causes or parts of abnormalities.
To address the above problem, there is a known technique for calculating the distance in waveform between the timeline data about the target process signal to be diagnosed and the case data stored in a case database, the distance being used to obtain the ratio of similarity therebetween to diagnose the state of the plant (see Patent Document 1).
Also, there is a known technique that presupposes the advance learning of a normal space and an abnormal space, the technique involving estimating a failure cause from the distance between the measured data to be diagnosed and the abnormal space (see Patent Document 2).